




'''simple docstring'''


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """conditional_detr"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }







   def __init__(						self   ,				UpperCamelCase=True   ,				UpperCamelCase=None   ,				UpperCamelCase=3   ,				UpperCamelCase=300   ,				UpperCamelCase=6   ,				UpperCamelCase=2048   ,				UpperCamelCase=8   ,				UpperCamelCase=6   ,				UpperCamelCase=2048   ,				UpperCamelCase=8   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.0   ,				UpperCamelCase=True   ,				UpperCamelCase="relu"   ,				UpperCamelCase=256   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.02   ,				UpperCamelCase=1.0   ,				UpperCamelCase=False   ,				UpperCamelCase="sine"   ,				UpperCamelCase="resnet50"   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=2   ,				UpperCamelCase=5   ,				UpperCamelCase=2   ,				UpperCamelCase=1   ,				UpperCamelCase=1   ,				UpperCamelCase=2   ,				UpperCamelCase=5   ,				UpperCamelCase=2   ,				UpperCamelCase=0.25   ,				**UpperCamelCase   ,				)					->     Dict:
     if backbone_config is not None and use_timm_backbone:
       raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'   )

     if not use_timm_backbone:
       if backbone_config is None:
         logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'   )
         __a								=       CONFIG_MAPPING['resnet'](out_features=['stage4']   )
       elif isinstance(UpperCamelCase   ,				UpperCamelCase   ):
         __a								=       backbone_config.get('model_type'   )
         __a								=       CONFIG_MAPPING[backbone_model_type]
         __a								=       config_class.from_dict(UpperCamelCase   )

     __a								=       use_timm_backbone
     __a								=       backbone_config
     __a								=       num_channels
     __a								=       num_queries
     __a								=       d_model
     __a								=       encoder_ffn_dim
     __a								=       encoder_layers
     __a								=       encoder_attention_heads
     __a								=       decoder_ffn_dim
     __a								=       decoder_layers
     __a								=       decoder_attention_heads
     __a								=       dropout
     __a								=       attention_dropout
     __a								=       activation_dropout
     __a								=       activation_function
     __a								=       init_std
     __a								=       init_xavier_std
     __a								=       encoder_layerdrop
     __a								=       decoder_layerdrop
     __a								=       encoder_layers
     __a								=       auxiliary_loss
     __a								=       position_embedding_type
     __a								=       backbone
     __a								=       use_pretrained_backbone
     __a								=       dilation
     # Hungarian matcher
     __a								=       class_cost
     __a								=       bbox_cost
     __a								=       giou_cost
     # Loss coefficients
     __a								=       mask_loss_coefficient
     __a								=       dice_loss_coefficient
     __a								=       cls_loss_coefficient
     __a								=       bbox_loss_coefficient
     __a								=       giou_loss_coefficient
     __a								=       focal_alpha
     super().__init__(is_encoder_decoder=UpperCamelCase   ,				**UpperCamelCase   )







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return self.encoder_attention_heads







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return self.d_model







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       copy.deepcopy(self.__dict__   )
     if self.backbone_config is not None:
       __a								=       self.backbone_config.to_dict()
     __a								=       self.__class__.model_type
     return output





class  __lowercase    (	__magic_name__			):
   _a    						=   version.parse("""1.11"""			)







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ('pixel_mask', {0: 'batch'}),
         ]   )







   @property
   def 		UpperCamelCase__      (						self   )					->     float:
     return 1e-5







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return 12









'''simple docstring'''


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     SCREAMING_SNAKE_CASE							(   a_						:				str = ""      ):
  __a								=       url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  __a								=       BeautifulSoup(requests.get(a_      ).text					,       'html.parser'      )
  __a								=       soup.find_all('td'					,       attrs='titleColumn'      )
  __a								=       soup.find_all('td'					,       class_='ratingColumn imdbRating'      )
  return {
      title.a.text: float(rating.strong.text      )
      for title, rating in zip(a_					,       a_      )
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				str = "IMDb_Top_250_Movies.csv"      ):
  __a								=       get_imdb_top_aaa_movies()
  with open(a_					,       'w'					,       newline=''      ) as out_file:
    __a								=       csv.writer(a_      )
    writer.writerow(['Movie title', 'IMDb rating']      )
    for title, rating in movies.items():
      writer.writerow([title, rating]      )


if __name__ == "__main__":
  write_movies()



